We shall strive more particularly here below in the document to describe the set of problems and issues that the inventors of the present patent application have confronted in the field of the monitoring of the respiratory flow of a patient on artificial respiration. It may be recalled that the respiratory flow corresponds to the volume of air flowing in the lungs per unit of time. The invention is naturally not limited to this particular field of application but is of interest for any technique of monitoring that has to cope with similar or proximate problems. Indeed, the present technique can be used to detect anomalies (also called irregularities or deviations) in relation to the “normal” (i.e. anomaly-free) behavior of any one of the following signals:                electrocardiogram (ECG) signals which are signals representing the progress of the electrical potential that commands the muscular activity of a patient's heart, as a function of time, measured by electrodes placed on the surface of the patient's skin;        electroencephalogram (EEG) signals which are signals representing the progress of the electrical activity of the brain, as a function of time, measured by electrodes placed on a patient's scalp;        signals representing the progress of arterial pressure as a function of time;        signals representing the progress of the oxygen concentration in tissues as a function of time;        signals representing the progress of intracranial pressure.        
This list is naturally not exhaustive and the present invention cannot be limited only to these fields of application. Indeed, it can be applied to any signal representing the progress of a patient's physiological parameters.
In the field of medical monitoring and artificial respiration, one vital parameter for which special monitoring has to be performed is that of monitoring of curves of the flow and pressure in the air passages. Indeed, in the case of incomplete or limited expiration, especially among patients with chronic obstructive pulmonary disease or with asthma, a phenomenon of air capture can arise causing thoracic distension. Thus, the lung pressure (Auto-PEEP or intrinsic positive and expiratory pressure) at the end of the expiration increases when such a phenomenon occurs. The presence of thoracic distension also results in the respiratory flow not returning to zero before the next inspiration begins.
This phenomenon of thoracic distension occurs in about 40% of patients under artificial respiration (or mechanical respiration) and it can have many harmful, physical consequences. Depending of the level of resistance and compliance of the patient's respiratory system, and therefore his time constant, clinically significant thoracic distension can occur gradually within a period of a few minutes.
It may be recalled that the goal of artificial respiration (or mechanical respiration) is to assist or replace a patient's spontaneous respiration if this respiration becomes inefficient or, in certain cases, totally absent. Artificial respiration is practiced mainly in the case of critical care (emergency medicine, intensive or intermediate care), but is also used in home care among patients having chronic respiratory deficiency.
This means that the detection of thoracic distension (i.e. the detection of Auto-PEEP) is important to enable the practitioner (or clinician) to take the action needed to reduce this phenomenon (for example by modifying the ventilator settings and extending the expiratory time).
PEEPi can only be quantified at specific points in time through the performance of an expiratory pause enabling measurement of the expiratory equilibrium pressure.
The progress of intra-pulmonary pressure can however be deduced from an analysis of the signal representing the progress of the air flow (in L/min) (i.e. the progress of the volume of the air inspired and expired by the patient as a function of time, also called the respiratory flow curve) of a patient measured through sensors positioned for example at the ventilator. This means that a thoracic distension (i.e. an Auto-Peep) can be detected through the study of such a signal. FIGS. 2(a) and 2(b) respectively present the characteristic phases (or segments) of such a signal during a respiration cycle comprising an inspiration, a pause and an expiration and a signal representing the progress of a patient's respiratory flow as a function of time, which includes a plurality of respiratory cycles.
There is a first technique known in the prior art, described in the US document US2010147305, called “System and Method for the Automatic Detection of the Expiratory Flow Limitation”, which can be used, through automated processing, to detect a limitation of flow in the patient.
However, this technique has various drawbacks, especially that of requiring the integration of numerous sensors (entailing a large amount of dead space) as well as the use of regular variations of ventilator parameters to enable this measurement. While this system can be envisaged in spontaneous ventilation and during an exploration of respiratory function, its use in an artificial ventilation circuit seems to be more complicated. Besides, this technique does not seem to be capable of enabling continuous and sequential analysis of the occurrence of the phenomenon of distension and is not suited to the detection of a thoracic distension related to a problem of interface between the patient and the ventilator.
There is also another technique known in the prior art, applied to the detection of anomalies in curves presenting the progress of the glucose level present in a patient's blood, described in the document by Y. Zhu, “Automatic Detection of Anomalies in Blood Glucose Using a Machine Learning Approach”, in IEEE International Conference on Information Reuse and Integration (IRI), 2010, which those skilled in the art could apply to the present case.
In addition, there is another technique also known in the prior art, applied to the detection of anomalies in encephalograms described in the document by Wulsin et al., “Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets”, Ninth International Conference on Machine Learning and Applications (ICMLA), 2010, which those skilled in the art could apply to the present case.
Finally, there is also another technique known in the prior art applied to the detection of anomalies described in the document by R. J. Riella et al., “Method for automatic detection of wheezing in lung sounds”, Brazilian Journal of Medical and Biological Research (2009) 42: 674-684, which those skilled in the art could apply to the present case.
One major drawback of these techniques lies in the fact that they require the implementation of a learning phase using a first data base followed by a validation phase using a second data base that is independent of the first data base.